A model approach revealed the relationship between banana pulp acidity and composition during growth and post harvest ripening
Introduction
Fruit acidity is a topic of primary importance in improving fruit quality since it influences the perception of both sourness and sweetness (Bugaud et al., 2011, Esti et al., 2002). These two attributes are major drivers of consumer preferences for fruit (Lyon et al., 1993), and are thus important traits to consider in breeding programs. Understanding the elaboration of fruit acidity is also important because acidity controls numerous enzyme activities (Madshus, 1988).
Fruit acidity is commonly measured using two chemical parameters: titratable acidity (TA) i.e. the amount of weakly bound hydrogen ions that can be released from the acids, and pH, the activity of free hydrogen ions. Fruit acidity is due to the acidity of the vacuole which represents about 90% of the volume of most mature fruit cells (Etxeberria et al., 2012). The acidity of the vacuole is the result of its ionic composition, mainly organic acids and mineral cations that determine the vacuolar pH and TA (Etienne et al., 2013). Banana pulp contains three major organic acids, malic acid, citric acid, and oxalic acid, whose concentrations undergo marked changes during growth and ripening (John and Marchal, 1995, Jullien et al., 2008) and phosphoric acid (Bugaud et al., 2013). Banana pulp contains soluble minerals, mainly potassium (K), and to a lesser extent magnesium (Mg), calcium (Ca), and chloride (Cl) (John and Marchal, 1995). During post harvest ripening, mineral content can still change due to migration between the peel and the pulp (Izonfuo and Omuaru, 1988).
There are considerable differences in pH and TA among dessert banana cultivars and among post-harvest ripening stages (Bugaud et al., 2013, Chacón et al., 1987), and the origins of these differences remain unclear. Quantifying the relations between pulp acidity and pulp ionic composition using a modeling approach, would advance our understanding of the determinants of banana acidity. Models of pH and TA predictions have been developed for peach (Lobit et al., 2002) and proved to be powerful tools to understand the mechanisms underlying changes in acidity during peach development. The objective of the present work was to apply and validate these models on banana fruit, in which other ionic species than those found in peach need to be taken into account, and to throw light on the determinants of the changes in pH and TA that occur during the life of the banana pulp, i.e. from growth on the plant through post harvest ripening.
Section snippets
pH model
The model used for pH prediction was adapted from Lobit et al. (2002). Banana pulp can be considered as a concentrated aqueous solution of weak acids, mainly malic, citric, oxalic and phosphoric acids, and mineral cations, mainly potassium, magnesium, calcium and chloride. Other acids can be found in banana pulp but were not taken into account in the present study. Weak acids are partly in free form and partly dissociated to form salts with monovalent cations. Proton exchange reactions occur
Changes in pulp acidity
Fruit age and the cultivar had a significant effect on pH and TA during fruit growth (Table 1). Throughout fruit growth, PL had the most acidic fruits (TA = 3.5 mEq 100 g FW−1 ± 0.22; pH = 5.5 ± 0.13), IDN 110 fruits were intermediate (TA = 2.8 mEq 100 g FW−1 ± 0.31; pH = 5.7 ± 0.17), and JB had the least acidic fruits (TA = 2.3 mEq 100 g FW−1 ± 0.27; pH = 5.9 ± 0.28) (Fig. 1A and B). In all three cultivars, TA decreased slightly during the early stages of fruit growth and then increased slightly. pH increased throughout fruit
Quality of prediction of the models
For the pH model, the lower the pH, the better the predictions, which explains why pH predictions were better during ripening than during fruit growth. This is due to the logarithm function of the pH which increases the sensitivity of the pH to input parameters with an increase in pH. Thus, imprecision in the determination of the chemical elements that are the main contributors to banana pulp acidity (organic acids and K) may be responsible for the difference between observed and predicted
Conclusions
This study, which presents a model of banana pulp acidity for the first time, showed that among acids, malic, citric and oxalic acids are the main contributors to banana pulp acidity, and that among soluble minerals, K also plays an important role. Consequently, studying the factors that affect malic acid, citric acid, oxalic acid, and K accumulation in banana pulp appears to be an appropriate area of research to ultimately modify banana fruit acidity. In future work, the pH model will be
Acknowledgment
Financial support for this study was provided by Structural European Funds.
References (27)
- et al.
Rheological and chemical predictors of texture and taste in dessert banana (Musa spp.)
Postharvest Biol. Technol.
(2013) - et al.
Physicochemical and sensory fruit characteristics of two sweet cherry cultivars after cool storage
Food Chem.
(2002) - et al.
In and out of the plant storage vacuole
Plant Sci.
(2012) Methods of Enzymatic Analysis
(1983)- et al.
Sensory characterisation enabled the first classification of dessert bananas
J. Sci. Food Agric.
(2011) - et al.
Escala físico-química de maduración de banano. Physico-chemical banana ripening scale
Fruits
(1987) Ion Association
(1962)- et al.
What controls fleshy fruit acidity? A review of malate and citrate accumulation in fruit cells
J. Exp. Bot.
(2013) - et al.
Linear Mixed-Effects Model, Linear Mixed-Effects Models Using R
(2013) - et al.
Virtual profiling: a new way to analyse phenotypes
Plant J.
(2010)
The microplate reader: an efficient tool for the separate enzymatic analysis of sugars in plant tissues—validation of a micro-method
J. Sci. Food Agric.
Effect of ripening on the chemical composition of Plantain peels and pulps (Musa paradisiaca)
J. Sci. Food Agric.
Ripening and biochemistry of the fruit
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